With the widespread use of information technologies, information networks are becoming
increasingly popular to capture complex relationships across various disciplines, such as …

Community detection in networks is one of the most popular topics of modern network
science. Communities, or clusters, are usually groups of vertices having higher probability of …

We provide quasi-experimental estimates of the impact of social media on mental health by
leveraging a unique natural experiment: the staggered introduction of Facebook across US …

Many widely used datasets for graph machine learning tasks have generally been
homophilous, where nodes with similar labels connect to each other. Recently, new Graph …

Graph Neural Networks (GNNs) are the predominant technique for learning over graphs.
However, there is relatively little understanding of why GNNs are successful in practice and …

There is increasing evidence suggesting neural networks' sensitivity to distribution shifts, so
that research on out-of-distribution (OOD) generalization comes into the spotlight …

Network embedding assigns nodes in a network to low-dimensional representations and
effectively preserves the network structure. Recently, a significant amount of progresses …

Network embedding, aiming to learn the low-dimensional representations of nodes in
networks, is of paramount importance in many real applications. One basic requirement of …

Local graph clustering methods aim to find a cluster of nodes by exploring a small region of
the graph. These methods are attractive because they enable targeted clustering around a …

Social network based applications have experienced exponential growth in recent years.
One of the reasons for this rise is that this application domain offers a particularly fertile …